Battery Current Estimation and Prediction During Charging with Ant Colony Optimization Algorithm
This paper presents an application of the Ant Colony Optimization (ACO) algorithm combined with the Logistic Regression (LR) method in the lead acid battery charging process. The ACO algorithm is used to obtain the best current pattern in the battery charging system to produce a smart charging syste...
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MDPI AG
2025-02-01
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| author | Selamat Muslimin Ekawati Prihatini Nyayu Latifah Husni Tresna Dewi Mukhidin Wartam Bin Umar Auvi Crisanta Ana Bela Sri Utami Handayani Wahyu Caesarendra |
| author_facet | Selamat Muslimin Ekawati Prihatini Nyayu Latifah Husni Tresna Dewi Mukhidin Wartam Bin Umar Auvi Crisanta Ana Bela Sri Utami Handayani Wahyu Caesarendra |
| author_sort | Selamat Muslimin |
| collection | DOAJ |
| description | This paper presents an application of the Ant Colony Optimization (ACO) algorithm combined with the Logistic Regression (LR) method in the lead acid battery charging process. The ACO algorithm is used to obtain the best current pattern in the battery charging system to produce a smart charging system with a fast and safe charging current for the battery. The best current pattern is conducted gradually and repeatedly to obtain termination in the form of the best current pattern according to the ACO algorithm. The results of the algorithm design produce a current pattern consisting of 10 A, 5 A, 3 A, 2 A, and 0 A. The charging system with this algorithm can charge all types of lead acid batteries. In this research, the capacity of battery 1’s State of Charge (SOC) is 56%, battery 2’s SOC is 62%, and battery 3’s SOC is 80%. When recharging the battery’s full condition to a SOC of 100%, the length of time for charging battery 1 for 12.73 min, battery 2 takes 15.73 min, and battery 3 takes 29.11 min. Smart charging with the ACO can charge the battery safely without current fluctuations compared to charging without an algorithm such that the amount of charging current used is not dangerous for the battery. In addition, data analysis is carried out to determine the value of accuracy in estimating SOC charging using supervised learning linear regression. The results of the data analysis with linear regression show that the battery’s SOC estimation has good accuracy, with an RMSE value of 0.32 and an MAE of 0.27. |
| format | Article |
| id | doaj-art-91d7f07483cf4aea85b1c09fe09d6d86 |
| institution | DOAJ |
| issn | 2673-6470 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Digital |
| spelling | doaj-art-91d7f07483cf4aea85b1c09fe09d6d862025-08-20T02:42:40ZengMDPI AGDigital2673-64702025-02-0151610.3390/digital5010006Battery Current Estimation and Prediction During Charging with Ant Colony Optimization AlgorithmSelamat Muslimin0Ekawati Prihatini1Nyayu Latifah Husni2Tresna Dewi3Mukhidin Wartam Bin Umar4Auvi Crisanta Ana Bela5Sri Utami Handayani6Wahyu Caesarendra7Department of Electrical Engineering, Sriwijaya State Polytechnic, Palembang 30139, IndonesiaDepartment of Electrical Engineering, Sriwijaya State Polytechnic, Palembang 30139, IndonesiaDepartment of Electrical Engineering, Sriwijaya State Polytechnic, Palembang 30139, IndonesiaDepartment of Electrical Engineering, Sriwijaya State Polytechnic, Palembang 30139, IndonesiaFaculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, BruneiDepartment of Electrical Engineering, Sriwijaya State Polytechnic, Palembang 30139, IndonesiaMechanical Design Engineering, Vocational School, Diponegoro University, Semarang 50275, IndonesiaDepartment of Mechanical Engineering, Faculty of Engineering and Science, Curtin University Malaysia, Lot 13149, Block 5 Kuala Baram Land District, CDT 250, Miri 98009, Sarawak, MalaysiaThis paper presents an application of the Ant Colony Optimization (ACO) algorithm combined with the Logistic Regression (LR) method in the lead acid battery charging process. The ACO algorithm is used to obtain the best current pattern in the battery charging system to produce a smart charging system with a fast and safe charging current for the battery. The best current pattern is conducted gradually and repeatedly to obtain termination in the form of the best current pattern according to the ACO algorithm. The results of the algorithm design produce a current pattern consisting of 10 A, 5 A, 3 A, 2 A, and 0 A. The charging system with this algorithm can charge all types of lead acid batteries. In this research, the capacity of battery 1’s State of Charge (SOC) is 56%, battery 2’s SOC is 62%, and battery 3’s SOC is 80%. When recharging the battery’s full condition to a SOC of 100%, the length of time for charging battery 1 for 12.73 min, battery 2 takes 15.73 min, and battery 3 takes 29.11 min. Smart charging with the ACO can charge the battery safely without current fluctuations compared to charging without an algorithm such that the amount of charging current used is not dangerous for the battery. In addition, data analysis is carried out to determine the value of accuracy in estimating SOC charging using supervised learning linear regression. The results of the data analysis with linear regression show that the battery’s SOC estimation has good accuracy, with an RMSE value of 0.32 and an MAE of 0.27.https://www.mdpi.com/2673-6470/5/1/6batterychargingAnt Colony Optimization (ACO)machine learning |
| spellingShingle | Selamat Muslimin Ekawati Prihatini Nyayu Latifah Husni Tresna Dewi Mukhidin Wartam Bin Umar Auvi Crisanta Ana Bela Sri Utami Handayani Wahyu Caesarendra Battery Current Estimation and Prediction During Charging with Ant Colony Optimization Algorithm Digital battery charging Ant Colony Optimization (ACO) machine learning |
| title | Battery Current Estimation and Prediction During Charging with Ant Colony Optimization Algorithm |
| title_full | Battery Current Estimation and Prediction During Charging with Ant Colony Optimization Algorithm |
| title_fullStr | Battery Current Estimation and Prediction During Charging with Ant Colony Optimization Algorithm |
| title_full_unstemmed | Battery Current Estimation and Prediction During Charging with Ant Colony Optimization Algorithm |
| title_short | Battery Current Estimation and Prediction During Charging with Ant Colony Optimization Algorithm |
| title_sort | battery current estimation and prediction during charging with ant colony optimization algorithm |
| topic | battery charging Ant Colony Optimization (ACO) machine learning |
| url | https://www.mdpi.com/2673-6470/5/1/6 |
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